SeLCA: Self-Supervised Learning of Canonical Axis

Robustness to rotation is critical for point cloud understanding tasks, as point cloud features can be affected dramatically with respect to prevalent rotation changes. In this work, we propose SeLCA, a novel self-supervised learning framework to learn to the canonical axis of point clouds in a probabilistic manner. In essence, we propose to \textit{learn} rotational-equivariance by predicting the canonical axis of point clouds, and achieve rotational-invariance by aligning the point clouds using their predicted canonical axis. When integrated into a rotation-sensitive pipeline, SeLCA achieves competitive performances on the ModelNet40 classification task under unseen rotations. Most interestingly, our proposed method also shows high robustness to various real-world point cloud corruptions presented by the ModelNet40-C dataset, compared to the state-of-the-art rotation-invariant method.